AIMC Topic: Molybdenum
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Constructing Built-In Electric Field in Hierarchical-Flower Heterostructure for High-Performance Serum Metabolic Assay.
Analytical chemistry
May 20, 2025
Laser desorption ionization mass spectrometry (LDI-MS) is a critical platform for high-throughput nontargeted metabolomics analysis in clinical diagnosis. However, traditional organic matrices inherently suffer from background interference in the low...
Heterojunction nanofluidic memristors based on peptide chain valves for neuromorphic applications.
Biosensors & bioelectronics
Apr 18, 2025
Memristors exhibit significant potential for neuromorphic computing due to their unique properties. This study introduces a heterojunction nanofluidic memristor (HJNFM) and explores its applications in simulating synapses and constructing neural netw...
Machine Learning Assisted Nanofluidic Array for Multiprotein Detection.
ACS nano
Feb 26, 2025
Solid-state nanopore and nanochannel biosensors have revolutionized protein detection by offering label-free, highly sensitive analyses. Traditional sensing systems (1st and 2nd stages) primarily focus on inner wall (IW) interactions, facing challeng...
A Machine Learning-Optimized System for Pulsatile, Photo- and Chemotherapeutic Treatment Using Near-Infrared Responsive MoS-Based Microparticles in a Breast Cancer Model.
ACS nano
Oct 27, 2024
Multimodal cancer therapies are often required for progressive cancers due to the high persistence and mortality of the disease and the negative systemic side effects of traditional therapeutic methods. Thus, the development of less invasive modaliti...
Sustainable separation of molybdenum from mixed mineral acids generated as semiconductor industry waste streams using tributyl phosphate (TBP) by effects of hybrid machine learning models.
Journal of environmental management
Oct 16, 2024
This study explores the separation and optimization of molybdenum (Mo) from mixed mineral acids derived from semiconductor industry waste streams with tributyl phosphate (TBP) by implementing machine learning (ML) models. Considerable experimental te...
Molybdenum Disulfide-Assisted Spontaneous Formation of Multistacked Gold Nanoparticles for Deep Learning-Integrated Surface-Enhanced Raman Scattering.
ACS nano
Jun 24, 2024
Several fabrication methods have been developed for label-free detection in various fields. However, fabricating high-density and highly ordered nanoscale architectures by using soluble processes remains a challenge. Herein, we report a biosensing pl...
Hybrid neuromorphic hardware with sparing 2D synapse and CMOS neuron for character recognition.
Science bulletin
Sep 7, 2023
Neuromorphic computing enables efficient processing of data-intensive tasks, but requires numerous artificial synapses and neurons for certain functions, which leads to bulky systems and energy challenges. Achieving functionality with fewer synapses ...
Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation.
Interdisciplinary sciences, computational life sciences
Jul 24, 2023
Breast cancer is commonly diagnosed with mammography. Using image segmentation algorithms to separate lesion areas in mammography can facilitate diagnosis by doctors and reduce their workload, which has important clinical significance. Because large,...
Multifunctional Optoelectronic Synapses Based on Arrayed MoS Monolayers Emulating Human Association Memory.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Apr 14, 2023
Optoelectronic synaptic devices integrating light-perception and signal-storage functions hold great potential in neuromorphic computing for visual information processing, as well as complex brain-like learning, memorizing, and reasoning. Herein, the...
Quasi-Volatile MoS Barristor Memory for 1T Compact Neuron by Correlative Charges Trapping and Schottky Barrier Modulation.
ACS applied materials & interfaces
Dec 13, 2022
Artificial neurons as the basic units of spiking neural network (SNN) have attracted increasing interest in energy-efficient neuromorphic computing. 2D transition metal dichalcogenide (TMD)-based devices have great potential for high-performance and ...
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